This paper presents a preliminary analysis of the sample complexity of theory revision within the framework of PAC (Probably Approximately Correct) learnability theory. By formalizing the notion that the initial theory is ``close'' to the correct theory we show that the sample complexity of an optimal propositional Horn-clause theory revision algorithm is $O( ( ln 1 / delta + d ln (s_0 + d + n) ) / epsilon)$, where $d$ is the syntactic distance between the initial and correct theories, $s_0$ is the size of initial theory, $n$ is the number of observable features, and $epsilon$ and $delta$ are the standard PAC error and probability bounds. The paper also discusses the problems raised by the computational complexity of theory revision.